OpenRaiser/Intern-Atlas huggingface dataset:
Cyber Security of Smart Grid Infrastructure
Venue: arXiv cs.CR 2014
Year: 2014

Smart grid security is crucial to maintain stable and reliable power system operation during the contingency situation due to the failure of any critical power system component. Ensuring a secured smart grid involves with a less possibility of power grid collapse or equipment malfunction. Due to lack of the proper security measures, a major blackout may occur which can even lead to a cascading failure. Therefore, to protect this critical power system infrastructure and to ensure a reliable and an uninterrupted power supply to the end users, smart grid security issues must be addressed with high priority. In a smart grid environment, electric power infrastructure is modernized by incorporating the current and future requirements and advanced functionalities to its consumers. To make the smart grid happen, cyber system is integrated with the physical power system. Although adoption of cyber system has made the grid more energy efficient and modernized, it has introduced cyber-attack issues which are critical for national infrastructure security and customer satisfaction. Due to the cyber-attack, power grid may face operational failures and loss of synchronization. This operational failure may damage critical power system components which may interrupt the power supply and make the system unstable resulting high financial penalties. In this chapter, some recent cyber attack related incidents into a smart grid environment are discussed. The requirements and the state of the art of cyber security issues of a critical power system infrastructure are illustrated elaborately.
$Y$-$Δ$ Product in 3-Way $Δ$ and Y-Channels for Cyclic Interference and Signal Alignment
Venue: arXiv cs.IT 2014
Year: 2014

In a full-duplex 3-way $Δ$ channel, three transceivers communicate to each other, so that a number of six messages is exchanged. In a $Y$-channel, however, these three transceivers are connected to an intermediate full-duplex relay. Loop-back self-interference is suppressed perfectly. The relay forwards network-coded messages to their dedicated users by means of interference alignment (IA) and signal alignment. A conceptual channel model with cyclic shifts described by a polynomial ring is considered for these two related channels. The maximally achievable rates in terms of the degrees of freedom measure are derived. We observe that the Y-channel and the 3-way $Δ$ channel provide a $Y$-$Δ$ product relationship. Moreover, we briefly discuss how this analysis relates to spatial IA and MIMO IA.
The Impact of Phase Fading on the Dirty Paper Channel
Venue: arXiv cs.IT 2014
Year: 2014

The impact of phase fading on the classical Costa dirty paper coding channel is studied. We consider a variation of this channel model in which the amplitude of the interference sequence is known at the transmitter while its phase is known at the receiver. Although the capacity of this channel has already been established, it is expressed using an auxiliary random variable and as the solution of a maximization problem. To circumvent the difficulty evaluating capacity, we derive alternative inner and outer bounds and show that the two expressions are to within a finite distance. This provide an approximate characterization of the capacity which depends only on the channel parameters. We consider, in particular, two distributions of the phase fading: circular binomial and circular uniform. The first distribution models the scenario in which the transmitter has a minimal uncertainty over the phase of the interference while the second distribution models complete uncertainty. For circular binomial fading, we show that binning with Gaussian signaling still approaches capacity, as in the channel without phase fading. In the case of circular uniform fading, instead, binning with Gaussian signaling is no longer effective and novel interference avoidance strategies are developed to approach capacity.
Nuclear Norm Subspace Identification (N2SID) for short data batches
Venue: arXiv cs.SY 2014
Year: 2014

Subspace identification is revisited in the scope of nuclear norm minimization methods. It is shown that essential structural knowledge about the unknown data matrices in the data equation that relates Hankel matrices constructed from input and output data can be used in the first step of the numerical solution presented. The structural knowledge comprises the low rank property of a matrix that is the product of the extended observability matrix and the state sequence and the Toeplitz structure of the matrix of Markov parameters (of the system in innovation form). The new subspace identification method is referred to as the N2SID (twice the N of Nuclear Norm and SID for Subspace IDentification) method. In addition to include key structural knowledge in the solution it integrates the subspace calculation with minimization of a classical prediction error cost function. The nuclear norm relaxation enables us to perform such integration while preserving convexity. The advantages of N2SID are demonstrated in a numerical open- and closed-loop simulation study. Here a comparison is made with another widely used SID method, i.e. N4SID. The comparison focusses on the identification with short data batches, i.e. where the number of measurements is a small multiple of the system order.
The Secret Life of a Process Description: A Look into the Evolution of a Large Process Model
Venue: arXiv cs.SE 2014
Year: 2014

Software process models must change continuously in order to remain consistent over time with the reality they represent, as well as relevant to the task they are intended for. Performing these changes in a sound and disci- plined fashion requires software process model evolution to be understood and controlled. The current situation can be characterized by a lack of understanding of software process model evolution and, in consequence, by a lack of systematic support for evolving software process models in organizations. This paper presents an analysis of the evolution of a large software process standard, namely, the process standard for the German Federal Government (V-Modell(R) XT). The analysis was performed with the Evolyzer tool suite, and is based on the complete history of over 600 versions that have been created during the development and maintenance of the standard. The analysis reveals similarities and differences between process evolution and empirical findings in the area of software system evolution. These findings provide hints on how to better manage process model evolution in the future.
Humanoid Robot With Vision Recognition Control System
Venue: arXiv cs.RO 2014
Year: 2014

This paper presents a solution to controlling humanoid robotic systems. The robot can be programmed to execute certain complex actions based on basic motion primitives. The humanoid robot is programmed using a PC. The software running on the PC can obtain at any given moment information about the state of the robot, or it can program the robot to execute a different action, providing the possibility of implementing a complex behavior. We want to provide the robotic system the ability to understand more on the external real world. In this paper we describe a method for detecting ellipses in real world images using the Randomized Hough Transform with Result Clustering. Real world images are preprocessed, noise reduction, greyscale transform, edge detection and finaly binarization in order to be processed by the actual ellipse detector. After all the ellipses are detected a post processing phase clusters the results.
Testing for separability is hard
Venue: arXiv cs.GT 2014
Year: 2014

This paper shows that it is computationally hard to decide (or test) if a consumption data set is consistent with separable preferences.
A quadratically tight partition bound for classical communication complexity and query complexity
Venue: arXiv cs.CC 2014
Year: 2014

In this work we introduce, both for classical communication complexity and query complexity, a modification of the 'partition bound' introduced by Jain and Klauck [2010]. We call it the 'public-coin partition bound'. We show that (the logarithm to the base two of) its communication complexity and query complexity versions form, for all relations, a quadratically tight lower bound on the public-coin randomized communication complexity and randomized query complexity respectively.
Mobile Adhoc Offloading
Venue: arXiv cs.NI 2014
Year: 2014

This problem is a series of biddings and auctions. Each round of bidding and auction are different from previous ones because of the change of network topology, variance of budget set by the sender, and possible evolution of strategies of other nodes. The huge strategy space of relay nodes makes the formulation to a game very difficult. We present a brief qualitative analysis in this paper, and propose a bidding strategy based on learning algorithms.
Strong edge-colouring of sparse planar graphs
Venue: arXiv cs.DM 2014
Year: 2014

A strong edge-colouring of a graph is a proper edge-colouring where each colour class induces a matching. It is known that every planar graph with maximum degree $Δ$ has a strong edge-colouring with at most $4Δ+4$ colours. We show that $3Δ+1$ colours suffice if the graph has girth 6, and $4Δ$ colours suffice if $Δ\geq 7$ or the girth is at least 5. In the last part of the paper, we raise some questions related to a long-standing conjecture of Vizing on proper edge-colouring of planar graphs.
Multidisciplinary Engineering Models: Methodology and Case Study in Spreadsheet Analytics
Venue: arXiv cs.SE 2014
Year: 2014

This paper demonstrates a methodology to help practitioners maximise the utility of complex multidisciplinary engineering models implemented as spreadsheets, an area presenting unique challenges. As motivation we investigate the expanding use of Integrated Resource Management(IRM) models which assess the sustainability of urban masterplan designs. IRM models reflect the inherent complexity of multidisciplinary sustainability analysis by integrating models from many disciplines. This complexity makes their use time-consuming and reduces their adoption. We present a methodology and toolkit for analysing multidisciplinary engineering models implemented as spreadsheets to alleviate such problems and increase their adoption. For a given output a relevant slice of the model is extracted, visualised and analysed by computing model and interdisciplinary metrics. A sensitivity analysis of the extracted model supports engineers in their optimisation efforts. These methods expose, manage and reduce model complexity and risk whilst giving practitioners insight into multidisciplinary model composition. We report application of the methodology to several generations of an industrial IRM model and detail the insight generated, particularly considering model evolution.
Combining Evaluation Metrics via the Unanimous Improvement Ratio and its Application to Clustering Tasks
Venue: arXiv cs.AI 2014
Year: 2014

Many Artificial Intelligence tasks cannot be evaluated with a single quality criterion and some sort of weighted combination is needed to provide system rankings. A problem of weighted combination measures is that slight changes in the relative weights may produce substantial changes in the system rankings. This paper introduces the Unanimous Improvement Ratio (UIR), a measure that complements standard metric combination criteria (such as van Rijsbergen's F-measure) and indicates how robust the measured differences are to changes in the relative weights of the individual metrics. UIR is meant to elucidate whether a perceived difference between two systems is an artifact of how individual metrics are weighted. Besides discussing the theoretical foundations of UIR, this paper presents empirical results that confirm the validity and usefulness of the metric for the Text Clustering problem, where there is a tradeoff between precision and recall based metrics and results are particularly sensitive to the weighting scheme used to combine them. Remarkably, our experiments show that UIR can be used as a predictor of how well differences between systems measured on a given test bed will also hold in a different test bed.
Robust Local Search for Solving RCPSP/max with Durational Uncertainty
Venue: arXiv cs.AI 2014
Year: 2014

Scheduling problems in manufacturing, logistics and project management have frequently been modeled using the framework of Resource Constrained Project Scheduling Problems with minimum and maximum time lags (RCPSP/max). Due to the importance of these problems, providing scalable solution schedules for RCPSP/max problems is a topic of extensive research. However, all existing methods for solving RCPSP/max assume that durations of activities are known with certainty, an assumption that does not hold in real world scheduling problems where unexpected external events such as manpower availability, weather changes, etc. lead to delays or advances in completion of activities. Thus, in this paper, our focus is on providing a scalable method for solving RCPSP/max problems with durational uncertainty. To that end, we introduce the robust local search method consisting of three key ideas: (a) Introducing and studying the properties of two decision rule approximations used to compute start times of activities with respect to dynamic realizations of the durational uncertainty; (b) Deriving the expression for robust makespan of an execution strategy based on decision rule approximations; and (c) A robust local search mechanism to efficiently compute activity execution strategies that are robust against durational uncertainty. Furthermore, we also provide enhancements to local search that exploit temporal dependencies between activities. Our experimental results illustrate that robust local search is able to provide robust execution strategies efficiently.
Unfounded Sets and Well-Founded Semantics of Answer Set Programs with Aggregates
Venue: arXiv cs.LO 2014
Year: 2014

Logic programs with aggregates (LPA) are one of the major linguistic extensions to Logic Programming (LP). In this work, we propose a generalization of the notions of unfounded set and well-founded semantics for programs with monotone and antimonotone aggregates (LPAma programs). In particular, we present a new notion of unfounded set for LPAma programs, which is a sound generalization of the original definition for standard (aggregate-free) LP. On this basis, we define a well-founded operator for LPAma programs, the fixpoint of which is called well-founded model (or well-founded semantics) for LPAma programs. The most important properties of unfounded sets and the well-founded semantics for standard LP are retained by this generalization, notably existence and uniqueness of the well-founded model, together with a strong relationship to the answer set semantics for LPAma programs. We show that one of the D-well-founded semantics, defined by Pelov, Denecker, and Bruynooghe for a broader class of aggregates using approximating operators, coincides with the well-founded model as defined in this work on LPAma programs. We also discuss some complexity issues, most importantly we give a formal proof of tractable computation of the well-founded model for LPA programs. Moreover, we prove that for general LPA programs, which may contain aggregates that are neither monotone nor antimonotone, deciding satisfaction of aggregate expressions with respect to partial interpretations is coNP-complete. As a consequence, a well-founded semantics for general LPA programs that allows for tractable computation is unlikely to exist, which justifies the restriction on LPAma programs. Finally, we present a prototype system extending DLV, which supports the well-founded semantics for LPAma programs, at the time of writing the only implemented system that does so. Experiments with this prototype show significant computational advantages of aggregate constructs over equivalent aggregate-free encodings.
Counting-Based Search: Branching Heuristics for Constraint Satisfaction Problems
Venue: arXiv cs.AI 2014
Year: 2014

Designing a search heuristic for constraint programming that is reliable across problem domains has been an important research topic in recent years. This paper concentrates on one family of candidates: counting-based search. Such heuristics seek to make branching decisions that preserve most of the solutions by determining what proportion of solutions to each individual constraint agree with that decision. Whereas most generic search heuristics in constraint programming rely on local information at the level of the individual variable, our search heuristics are based on more global information at the constraint level. We design several algorithms that are used to count the number of solutions to specific families of constraints and propose some search heuristics exploiting such information. The experimental part of the paper considers eight problem domains ranging from well-established benchmark puzzles to rostering and sport scheduling. An initial empirical analysis identifies heuristic maxSD as a robust candidate among our proposals.eWe then evaluate the latter against the state of the art, including the latest generic search heuristics, restarts, and discrepancy-based tree traversals. Experimental results show that counting-based search generally outperforms other generic heuristics.
Consistency Techniques for Flow-Based Projection-Safe Global Cost Functions in Weighted Constraint Satisfaction
Venue: arXiv cs.AI 2014
Year: 2014

Many combinatorial problems deal with preferences and violations, the goal of which is to find solutions with the minimum cost. Weighted constraint satisfaction is a framework for modeling such problems, which consists of a set of cost functions to measure the degree of violation or preferences of different combinations of variable assignments. Typical solution methods for weighted constraint satisfaction problems (WCSPs) are based on branch-and-bound search, which are made practical through the use of powerful consistency techniques such as AC*, FDAC*, EDAC* to deduce hidden cost information and value pruning during search. These techniques, however, are designed to be efficient only on binary and ternary cost functions which are represented in table form. In tackling many real-life problems, high arity (or global) cost functions are required. We investigate efficient representation scheme and algorithms to bring the benefits of the consistency techniques to also high arity cost functions, which are often derived from hard global constraints from classical constraint satisfaction. The literature suggests some global cost functions can be represented as flow networks, and the minimum cost flow algorithm can be used to compute the minimum costs of such networks in polynomial time. We show that naive adoption of this flow-based algorithmic method for global cost functions can result in a stronger form of null-inverse consistency. We further show how the method can be modified to handle cost projections and extensions to maintain generalized versions of AC* and FDAC* for cost functions with more than two variables. Similar generalization for the stronger EDAC* is less straightforward. We reveal the oscillation problem when enforcing EDAC* on cost functions sharing more than one variable. To avoid oscillation, we propose a weak version of EDAC* and generalize it to weak EDGAC* for non-binary cost functions. Using various benchmarks involving the soft variants of hard global constraints ALLDIFFERENT, GCC, SAME, and REGULAR, empirical results demonstrate that our proposal gives improvements of up to an order of magnitude when compared with the traditional constraint optimization approach, both in terms of time and pruning.
Drake: An Efficient Executive for Temporal Plans with Choice
Venue: arXiv cs.AI 2014
Year: 2014

This work presents Drake, a dynamic executive for temporal plans with choice. Dynamic plan execution strategies allow an autonomous agent to react quickly to unfolding events, improving the robustness of the agent. Prior work developed methods for dynamically dispatching Simple Temporal Networks, and further research enriched the expressiveness of the plans executives could handle, including discrete choices, which are the focus of this work. However, in some approaches to date, these additional choices induce significant storage or latency requirements to make flexible execution possible. Drake is designed to leverage the low latency made possible by a preprocessing step called compilation, while avoiding high memory costs through a compact representation. We leverage the concepts of labels and environments, taken from prior work in Assumption-based Truth Maintenance Systems (ATMS), to concisely record the implications of the discrete choices, exploiting the structure of the plan to avoid redundant reasoning or storage. Our labeling and maintenance scheme, called the Labeled Value Set Maintenance System, is distinguished by its focus on properties fundamental to temporal problems, and, more generally, weighted graph algorithms. In particular, the maintenance system focuses on maintaining a minimal representation of non-dominated constraints. We benchmark Drakes performance on random structured problems, and find that Drake reduces the size of the compiled representation by a factor of over 500 for large problems, while incurring only a modest increase in run-time latency, compared to prior work in compiled executives for temporal plans with discrete choices.
Efficient Codes for Adversarial Wiretap Channels
Venue: arXiv cs.IT 2014
Year: 2014

In [13] we proposed a (ρ_r , ρ_w )-adversarial wiretap channel model (AWTP) in which the adversary can adaptively choose to see a fraction ρ_r of the codeword sent over the channel, and modify a fraction ρ_w of the codeword by adding arbitrary noise values to them. In this paper we give the first efficient construction of a capacity achieving code family that provides perfect secrecy for this channel.
Visual Tracking using Particle Swarm Optimization
Venue: arXiv cs.CV 2014
Year: 2014

The problem of robust extraction of visual odometry from a sequence of images obtained by an eye in hand camera configuration is addressed. A novel approach toward solving planar template based tracking is proposed which performs a non-linear image alignment for successful retrieval of camera transformations. In order to obtain global optimum a bio-metaheuristic is used for optimization of similarity among the planar regions. The proposed method is validated on image sequences with real as well as synthetic transformations and found to be resilient to intensity variations. A comparative analysis of the various similarity measures as well as various state-of-art methods reveal that the algorithm succeeds in tracking the planar regions robustly and has good potential to be used in real applications.